Introduction

nf-CBRA-snvs (nf-core - CIBERER Bioinformatics for Rare diseases Analysis - Small Nucleotide Variant) is a workflow optimized for the analysis of rare diseases, designed to detect SNVs and INDELs in targeted sequencing data (CES/WES) as well as whole genome sequencing (WGS).

This pipeline is developed using Nextflow, a workflow management system that enables an easy execution across various computing environments. It uses Docker or Singularity containers, simplifying setup and ensuring reproducibility of results. The pipeline assigns a container to each process, which simplifies the management and updating of software dependencies. When possible, processes are sourced from nf-core/modules, promoting reusability across all nf-core pipelines and contributing to the broader Nextflow community.

Pipeline summary

The pipeline can perform the following steps:

• Mapping of the reads to reference (BWA-MEM)
• Process BAM file (GATK MarkDuplicates, GATK BaseRecalibrator and GATK ApplyBQSR)

• Variant calling with the following tools:

  • GATK4 Haplotypecaller (run_gatk = true). This subworkflow includes:
  • GATK4 Haplotypecaller.
  • Hard Filters and VarianFiltration to mark PASS variants. More information here.
  • Bcftools Filter to keep PASS variants on chr1-22, X, Y.
  • Split Multialletic.
  • Dragen (run_dragen = true). This subworkflow includes:
  • GATK4 Calibratedragstrmodel
  • GATK4 Haplotypecaller with --dragen-mode.
  • VarianFiltration with --filter-expression "QUAL < 10.4139" --filter-name "DRAGENHardQUAL"to mark PASS variants. More information here.
  • Bcftools Filter to keep PASS variants on chr1-22, X, Y.
  • Split Multialletic.
  • DeepVariant (run_deepvariant = true). This subworkflow includes:
  • DeepVariant makeexamples: Converts the input alignment file to a tfrecord format suitable for the deep learning model.
  • DeepVariant callvariants: Call variants based on input tfrecords. The output is also in tfrecord format, and needs postprocessing to convert it to vcf.
  • DeepVariant postprocessvariants: Convert variant calls from callvariants to VCF, and also create GVCF files based on genomic information from makeexamples. More information here.
  • Bcftools Filter to keep PASS variants on chr1-22, X, Y.
  • Split Multialletic.

• Merge and integration of the vcfs obtained with the different tools.

• Annotation of the variants:

  • Regions of homozygosity (ROHs) with AUTOMAP
  • Effect of the variants with Ensembl VEP using the flag --everything, which includes the following options: --sift b, --polyphen b, --ccds, --hgvs, --symbol, --numbers, --domains, --regulatory, --canonical, --protein, --biotype, --af, --af_1kg, --af_esp, --af_gnomade, --af_gnomadg, --max_af, --pubmed, --uniprot, --mane, --tsl, --appris, --variant_class, --gene_phenotype, --mirna
  • Postvep format VEP tab demilited output and filter variants by minor allele frequency (--maf).
  • You can enhance the annotation by incorporating gene rankings from GLOWgenes, a network-based algorithm developed to prioritize novel candidate genes associated with rare diseases. Precomputed rankings based on PanelApp gene panels are available here. To include a specific GLOWgenes ranking, use the option --glowgenes_panel (path to the panel.txt), for example: --glowgenes_panel https://raw.githubusercontent.com/TBLabFJD/GLOWgenes/refs/heads/master/precomputed_panelAPP/GLOWgenes_prioritization_Neurological_ciliopathies_GA.txt. Additionally, you can include the Gene-Disease Specificity Score (SGDS) using: --glowgenes_sgds https://raw.githubusercontent.com/TBLabFJD/GLOWgenes/refs/heads/master/SGDS.csv. This score ranges from 0 to 1, where 1 indicates a gene ranks highly for only a few specific diseases (high specificity), and 0 indicates the gene consistently ranks highly across many diseases (low specificity).

Usage

First, prepare a samplesheet with your input data:

sample,fastq_1,fastq_2 SAMPLE_PAIRED_END,/path/to/fastq/files/AEG588A1_S1_L002_R1_001.fastq.gz,/path/to/fastq/files/AEG588A1_S1_L002_R2_001.fastq.gz

Each row represents a pair of paired end fastq files.

You can run the pipeline using:

nextflow run nf-cbra-snvs/main.nf \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir <OUTDIR>

For more details and further functionality, please refer to the usage documentation.

Pipeline output

For details about the output files and reports, please refer to the output documentation.

Credits

nf-CBRA-snvs was developed within the framework of a call for intramural cooperative and complementary actions (ACCI) funded by CIBERER (Biomedical Research Network Centre for Rare Diseases).

Main Developer - Yolanda Benítez Quesada

Coordinator - Carlos Ruiz Arenas

Other contributors - Graciela Uría Regojo - Pedro Garrido Rodríguez - Rafa Farias Varona - Pablo Minguez - Daniel Lopez